Advances in technology have allowed many business processes to become automated. Such processes may be connected to activities such as operations, sales, marketing, governance, product development, and so forth. Some specific examples of processes include underwriting, loan origination, benefits administration, customer service, product change requests, complaint management, quality management, and many others.
These processes generate an enormous amount of data. In order to understand the data, reports which summarize, aggregate, and analyze the data are needed. A proper report can inform managers what products customers are buying, how long it takes to resolve issues, and forecast sales trends—just to name a few examples. However, creating a report is often a complex task.
For example, a user may create a data set for a report by issuing a direct query against the database where this data is stored. However, this technique generally requires that the user be very familiar with query language (e.g., SQL, DQL, XQuery, and the like) and also the underlying schema of the data. Further, when creating a report, the user generally starts from scratch and may not have any knowledge about what has been done previously. This can be very overwhelming, especially when the user is non-technical, is new, and must select data from a very large and complicated schema in order to create a report.
Therefore, there is a need to provide improved report creation techniques that allow non-technical users to easily create meaningful and useful reports.